Method and apparatus used in node for wireless communication measurement

Abstract

The present application discloses a method and apparatus used in a node for wireless communication measurement. A first node receives a first information block, wherein the first information block configures a first measurement for mobility, and the number of computing power units occupied by the first measurement is equal to a first value; and only when the first value is not greater than the number of remaining computing power units of the first node, the first node processes the first measurement. The number of remaining computing power units is equal to the total number of computing power units of the first node minus the number of computing power units that have already been occupied. The total number of computing power units of the first node depends on the number of first-type resources comprised in the first node and the number of second-type resources comprised in the corresponding first-type resources. Any first-type resource comprised in the first node comprises at least one second-type resource. The present application optimizes computing power allocation so as to avoid resource waste and ensure the reliability of measurement.

Description

Method and apparatus in a node used for wireless communication measurement

[0001] This application claims priority to the Chinese Patent Application No. 202411066930.7, filed on August 5, 2024, and entitled "Method and apparatus in a node used for wireless communication measurement", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to a signal transmission method and apparatus in a wireless communication system, and in particular to a measurement method and apparatus. BACKGROUND

[0003] The application scenarios of future wireless communication systems are increasingly diversified. In order to meet the different performance requirements of different scenarios, 3GPP (3rd Generation Partner Project) is actively studying how to combine AI (Artificial Intelligence) / ML (Machine Learning) technology with mobile communication. The main application scenarios include network automation, optimized resource allocation, and improved service continuity for mobile users, etc.

[0004] Mobility of user equipment (UE) is an important feature of wireless networks. In the connected mode of the UE, the current measurements for mobility mainly include intra-frequency NR (New Radio) measurements, inter-frequency NR measurements, inter-RAT (Radio Access Technology) measurements for E-UTRA (Evolved UMTS Terrestrial Radio Access Network), and inter-RAT measurements for UTRA (UMTS Terrestrial Radio Access Network). In addition, in the idle mode and inactive mode of the UE, the UE also needs to perform measurements for cell selection and cell reselection to maintain the connection to the base station.

[0005] Meanwhile, AI / ML can achieve load balancing and energy saving by analyzing and predicting resource state information such as physical resource block utilization of neighboring cells and serving cells, number of active UEs, etc. In addition, AI / ML can optimize wireless resource management strategies by predicting the moving trajectory of UEs, enabling the network to allocate resources and make handover decisions based on the expected movement path of UEs. AI / ML-based mobility management not only helps to improve the accuracy and efficiency of handover, but also reduces the handover interruption time and improves user experience. In the future, 3GPP will further deepen the application of AI / ML in mobility management, promote the deep integration of AI / ML and communication networks, and improve the intelligent level of the network, optimize mobility management, and improve spectrum and energy efficiency to provide users with more stable and efficient mobile communication services. SUMMARY

[0006] Currently, in the Release-18 standard, the processing of physical layer channel information is defined with clear resource occupation and corresponding priority design, that is, when the remaining CPU (Central Processing Unit) resources are not enough to meet the number of CPUs required for channel processing, the processing of corresponding channel information will be abandoned. The processing of physical layer channel information itself has high requirements for latency. In the future, when AI / ML is used for mobility management measurement, because the requirements of AI / ML calculation and inference for hardware and resource occupation will be more stringent, how to reasonably define AI / ML computing power or resources is a problem that needs to be considered.

[0007] To solve the above problems, the present application discloses a solution. It should be noted that although a large number of embodiments of the present application are developed for AI / ML, the present application is also applicable to other schemes, such as traditional channel information reporting schemes. In addition, the use of a unified solution in different scenarios (including but not limited to AI / ML-based schemes and traditional information reporting schemes) helps to reduce hardware complexity and cost. In the case of no conflict, the embodiments in the first node and the features in the embodiments of the present application can be applied to the second node, and vice versa. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0008] In particular, the explanation of the terminology, nouns, functions, variables in the present application (if not specially stated) can refer to the definitions in TS38 series, TS37 series in the technical standards (TS) of 3GPP (the 3rd Generation Partnership Project). If necessary, TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, TS38.423 in the technical standards of 3GPP can be referred to for the understanding of the present application.

[0009] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement TS38 series of 3GPP.

[0010] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement TS37 series of 3GPP.

[0011] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement Rel-17 version of 3GPP.

[0012] As an embodiment, the explanation of the terminology in the present application refers to the definitions in the specification agreement Rel-18 version of 3GPP.

[0013] The present application discloses a method of a first node for wireless communication measurement, comprising:

[0014] receiving a first information block, the first information block configuring a first measurement for mobility, the number of computing unit occupied by the first measurement for mobility being equal to a first value;

[0015] only when the first value is not greater than the number of remaining computing units of the first node, processing the first measurement for mobility;

[0016] wherein the number of remaining computing units of the first node is equal to the total number of computing units of the first node minus the number of computing units already occupied; the total number of computing units of the first node depends on the number of first type resources included in the first node, and the number of second type resources included in the corresponding first type resources; any one first type resource included in the first node includes at least one second type resource.

[0017] As an embodiment, the problem to be solved by the present application includes the principle of allocating computing power corresponding to high-level measurement.

[0018] As an embodiment, the problem to be solved by the present application includes how to reasonably allocate computing power to improve performance when AI / ML computing power is used for high-level measurement and prediction.

[0019] As an embodiment, the features of the above method include defining the number of occupied computing power corresponding to the measurement for mobility management, and then reasonably allocating resources when AI / ML is used for measurement and prediction for mobility management, and ensuring that the base station and the terminal can keep consistent in understanding the occupation of computing power.

[0020] As an embodiment, the features of the above method include reasonably allocating AI / ML computing power to improve overall system performance.

[0021] According to an aspect of the present application, the features of the above method are that the first type of resources correspond to the processing units included in the first node, and the second type of resources correspond to a process supported by one processing unit included in the first node; and the total number of computing power units of the first node corresponds to the total number of processes supported by all processing units included in the first node.

[0022] As an embodiment, the features of the above method include having the ability of parallel computing and parallel processing for AI / ML computing, defining AI / ML computing power as first type of resources and second type of resources, and defining the minimum unit of computing power as one second type of resource, to further make full use of the ability of AI / ML parallel processing, and to ensure the optimized use of resources.

[0023] According to an aspect of the present application, the features of the above method are that the first value depends on the ID of the first model, the first model is used for the first measurement for mobility, and the first model is based on AI / ML.

[0024] As an embodiment, the features of the above method include defining different numbers of occupied computing power for different models, and the model with more occupied computing power has better performance but also occupies more resources, so as to achieve a better balance between performance and cost, and to improve performance while reasonably using resources.

[0025] According to an aspect of the present application, the features of the above method are that the first value depends on the ID of the first model, the first model is used for the first measurement for mobility, and the first model is based on AI / ML.

[0026] Receiving a second information block, the second information block is configured for a second measurement for mobility, and the number of computing power units occupied by the second measurement for mobility is equal to a second value;

[0027] The second measurement is for a priority lower than a priority for which the first measurement is for. The first value is not greater than the number of remaining computing capability units of the first node. The second value is not greater than the number of remaining computing capability units of the first node. A sum of the first value and the second value is greater than the number of remaining computing capability units of the first node. The second measurement for mobility is not processed.

[0028] As an embodiment, the method has a feature that different priorities are defined for different measurements, so that when computing resources are insufficient, measurements with lower priorities are abandoned and reported, so as to save resources.

[0029] As an embodiment, the method has a feature that the base station and the terminal keep consistent in defining priorities, so that the base station can know which measurement with a lower priority is abandoned, so as to avoid incorrect reception of measurement reports on the base station side.

[0030] According to an aspect of the present application, the method has a feature that the first measurement for mobility is one measurement in a first measurement set, and the first measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

[0031] According to an aspect of the present application, the method has a feature that the first measurement for mobility is one measurement in a second measurement set, and the second measurement set includes at least one of cell selection, cell reselection, and cell handover.

[0032] According to an aspect of the present application, the method has a feature that the first value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility. The first node does not assume that computing capability units occupied by the first measurement for mobility are located in two different first resources.

[0033] As an embodiment, the method has a feature that one measurement does not simultaneously occupy a second resource in two first resources, so as to avoid interaction between two first resources for AI / ML calculation of one measurement, so as to improve calculation efficiency and avoid introducing too high complexity.

[0034] According to an aspect of the present application, the method has a feature that it includes:

[0035] The first information set is transmitted.

[0036] The first information set indicates a number of first-type resources included in the first node, and a number of second-type resources included in the corresponding first-type resources.

[0037] As an embodiment, the features of the above method include that the first node reports the number of computing power to help the network side to reasonably configure the measurement, so as to avoid the scene of waste of computing power and insufficient computing power.

[0038] According to an aspect of the present application, the features of the above method are that, comprising:

[0039] Receiving a third information block;

[0040] The first value depends on the indication of the third information block.

[0041] As an embodiment, the ID is Identify.

[0042] As an embodiment, the ID is Identification.

[0043] As an embodiment, the ID is Identity.

[0044] As an embodiment, the ID is Identifier.

[0045] As an embodiment, the ID is identity.

[0046] As an embodiment, the ID is identification.

[0047] As an embodiment, the ID includes an AI / ML model ID.

[0048] As an embodiment, the ID includes an AI / ML function ID.

[0049] As an embodiment, the ID includes an AI / ML entity ID.

[0050] As an embodiment, the first node is a user equipment.

[0051] As an embodiment, the first node is a terminal.

[0052] The present application discloses a method in a second node for wireless communication measurement, comprising:

[0053] Sending a first information block, the first information block is configured for the first measurement of mobility, the number of computing power units occupied by the first measurement for mobility is equal to the first value;

[0054] The receiver of the first information block includes a first node; the first node processes the first measurement for mobility only when the first value is not greater than the remaining computing capability unit number of the first node; the remaining computing capability unit number of the first node is equal to the total computing capability unit number of the first node minus the number of computing capability units that have been occupied; the total computing capability unit number of the first node depends on the number of first-type resources included in the first node and the number of second-type resources included in the corresponding first-type resources; at least one second-type resource is included in any first-type resource included in the first node.

[0055] According to an aspect of the present application, the above method is characterized in that the first-type resources correspond to processing units included in the first node, and the second-type resources correspond to a process supported by a processing unit included in the first node; and the total computing capability unit number of the first node corresponds to the total number of processes supported by all processing units included in the first node.

[0056] According to an aspect of the present application, the above method is characterized in that the first value depends on the ID of a first model used for the first measurement for mobility, and the first model is based on AI / ML.

[0057] According to an aspect of the present application, the above method is characterized in that it includes:

[0058] sending a second information block, the second information block configuring a second measurement for mobility, the number of computing capability units occupied by the second measurement for mobility being equal to a second value;

[0059] The priority to which the second measurement is directed is lower than the priority to which the first measurement is directed; the first value is not greater than the remaining computing capability unit number of the first node; the second value is not greater than the remaining computing capability unit number of the first node; the sum of the first value and the second value is greater than the remaining computing capability unit number of the first node; and the second measurement for mobility is not processed by the first node.

[0060] According to an aspect of the present application, the above method is characterized in that the first measurement for mobility is one measurement in a first-type measurement set, and the first-type measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

[0061] According to an aspect of the present application, the above method is characterized in that the first measurement for mobility is one measurement in a second measurement set, the second measurement set comprising at least one of cell selection, cell reselection, and cell handover.

[0062] According to an aspect of the present application, the above method is characterized in that the first number is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; and the first node does not assume that the computing capability unit occupied by the first measurement for mobility is located in two different first resources.

[0063] According to an aspect of the present application, the above method is characterized in that it comprises:

[0064] receiving a first information set;

[0065] The first information set indicates the number of first resources included in the first node, and the number of second resources included in the corresponding first resources.

[0066] According to an aspect of the present application, the above method is characterized in that it comprises:

[0067] sending a third information block;

[0068] The first number depends on the indication of the third information block.

[0069] As an embodiment, the second node is a base station.

[0070] As an embodiment, the second node is an eNB.

[0071] As an embodiment, the second node is a gNB.

[0072] The present application discloses a device of a first node for wireless communication measurement, comprising:

[0073] a first receiver receiving a first information block, the first information block configuring a first measurement for mobility, the number of computing capability units occupied by the first measurement for mobility being equal to a first number;

[0074] The first receiver only processes the first measurement for mobility when the first number is not greater than the number of remaining computing capability units of the first node.

[0075] The remaining computing capability unit number of the first node is equal to the total computing capability unit number of the first node minus the number of computing capability units that have been occupied; the total computing capability unit number of the first node depends on the number of first-type resources included in the first node and the number of second-type resources included in the corresponding first-type resources; any one first-type resource included in the first node includes at least one second-type resource.

[0076] The application discloses a device of a second node for wireless communication measurement, comprising:

[0077] The second transmitter transmits a first information block, and the first information block is configured for a first measurement for mobility, and the number of computing capability units occupied by the first measurement for mobility is equal to a first value;

[0078] The receiver of the first information block includes a first node; only when the first value is not greater than the remaining computing capability unit number of the first node, the first node processes the first measurement for mobility; the remaining computing capability unit number of the first node is equal to the total computing capability unit number of the first node minus the number of computing capability units that have been occupied; the total computing capability unit number of the first node depends on the number of first-type resources included in the first node and the number of second-type resources included in the corresponding first-type resources; any one first-type resource included in the first node includes at least one second-type resource.

[0079] As an embodiment, compared with the conventional scheme, the application has the following advantages which are not limited to:

[0080] The application supports applying AI / ML to high-layer measurement, and different computing power consumption is defined for different measurements to reasonably allocate computing power resources, considering that the time required for AI / ML calculation for high-layer measurement is long and the problem of large amount of calculation consumption exists;

[0081] The first-type resources and the second-type resources are defined to match the different needs of AI / ML calculation for computing speed, storage space and storage speed and the extensive use of parallel computing, so as to fully utilize the computing power resources;

[0082] The consensus between the base station and the terminal is established to ensure the reliable transmission of the reported information;

[0083] The fusion of AI and communication is supported to improve the adaptability and intelligent level of the communication system, and thus the performance, efficiency and user experience of the communication system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0084] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in connection with the following accompanying drawings:

[0085] Figure 1 shows a flow diagram of transmissions by a first node according to one embodiment of the application;

[0086] Figure 2 shows a schematic diagram of a network architecture according to one embodiment of the application;

[0087] Figure 3 shows a schematic diagram of an embodiment of a radio protocol architecture for the user and control planes according to one embodiment of the application;

[0088] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to one embodiment of the application;

[0089] Figure 5 shows a flow diagram of transmissions between a first node and a second node according to one embodiment of the application;

[0090] Figure 6 shows a flow diagram of transmissions between a first node and a second node according to another embodiment of the application;

[0091] Figure 7 shows a flow diagram of transmissions between a first node and a second node according to yet another embodiment of the application;

[0092] Figure 8 shows a schematic diagram of a first type of resource and a second type of resource according to one embodiment of the application;

[0093] Figure 9 shows a schematic diagram of a first measurement according to one embodiment of the application;

[0094] Figure 10 shows a schematic diagram of a first type of resource occupation according to one embodiment of the application;

[0095] Figure 11 shows a schematic diagram of RAN domain AI / ML function deployment according to one embodiment of the application;

[0096] Figure 12 shows a schematic diagram of AI / ML function deployment for a UE according to one embodiment of the application;

[0097] Figure 13 shows a schematic diagram of an artificial intelligence or machine learning based processing system according to one embodiment of the application;

[0098] Figure 14 shows a schematic diagram of artificial intelligence or machine learning according to one embodiment of the application;

[0099] Figure 15 shows a structural block diagram of a processing apparatus for use in a first node according to one embodiment of the application;

[0100] Figure 16 shows a structural block diagram of a processing device in a second node according to an embodiment of the present application. DETAILED DESCRIPTION

[0101] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Based on performance, flexibility, complexity, overhead and compatibility, etc., the person skilled in the art has the motivation to combine the embodiments in different drawings flexibly without conflict, including but not limited to the embodiments in Figure 1 and the embodiments in Figures 5 to 14, the embodiments in Figure 5 and the embodiments in Figures 6 to 14, etc.

[0102] Embodiment 1

[0103] Embodiment 1 illustrates a flowchart of a first node transmission according to an embodiment of the present application, as shown in Figure 1. In Figure 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific time sequence between the steps.

[0104] The first node receives a first information block in step 101, the first information block is configured for a first measurement for mobility, and the number of computing unit occupied by the first measurement for mobility is equal to a first value; and processes the first measurement for mobility only when the first value is not greater than the number of remaining computing units of the first node in step 102.

[0105] In Embodiment 1, the number of remaining computing units of the first node is equal to the total number of computing units of the first node minus the number of computing units already occupied; the total number of computing units of the first node depends on the number of first-type resources included in the first node, and the number of second-type resources included in the corresponding first-type resources; any one first-type resource included in the first node includes at least one second-type resource.

[0106] As an embodiment, the first information block is transmitted through RRC (Radio Resource Control) signaling.

[0107] As an embodiment, the first information block includes one or more RRC IE (Information Elements).

[0108] As an embodiment, the first information block includes one or more fields in one RRC IE.

[0109] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises CSI (Channel State Information).

[0110] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises CSI-RS (Channel State Information Reference Signal).

[0111] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises Report.

[0112] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises Config.

[0113] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises Meas.

[0114] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises Measurement.

[0115] As one embodiment, the name of the RRC signaling used to transmit the first information block comprises Object.

[0116] As one embodiment, the first information block comprises a CSI-ReportConfig IE.

[0117] As one embodiment, the first information block comprises a CSI-ReportSubConfig IE.

[0118] As one embodiment, the first information block comprises a ServingCellConfig IE.

[0119] As one embodiment, the first information block comprises one or more fields in the ServingCellConfig IE.

[0120] As one embodiment, the first information block comprises a CSI-MeasConfig IE.

[0121] As one embodiment, the first information block comprises one or more fields in the CSI-MeasConfig IE.

[0122] As one embodiment, the first information block comprises a CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0123] As one embodiment, the first information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0124] As one embodiment, the first information block includes the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0125] As one embodiment, the first information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0126] As one embodiment, the first information block includes the CSI-ReportSubConfigTriggerList IE.

[0127] As one embodiment, the first information block includes one or more fields in the CSI-ReportSubConfigTriggerList IE.

[0128] As one embodiment, the first information block includes the CSI-ReportConfig IE.

[0129] As one embodiment, the first information block includes one or more fields in the CSI-ReportConfig IE.

[0130] As one embodiment, the first information block includes the CSI-ReportSubConfig IE.

[0131] As one embodiment, the first information block includes one or more fields in the CSI-ReportSubConfig IE.

[0132] As one embodiment, the first information block includes one or more fields in the CSI-AperiodicTriggerStateList IE.

[0133] As one embodiment, the first information block includes one or more fields in the CSI-IM-Resource IE.

[0134] As one embodiment, the first information block includes one or more fields in the CSI-IM-ResourceSet IE.

[0135] As one embodiment, the first information block includes one or more fields in the CSI-ResourceConfig IE.

[0136] As one embodiment, the first information block comprises one or more fields in a CSI-RS-ResourceConfigMobility IE.

[0137] As one embodiment, the first information block comprises one or more fields in a NZP-CSI-RS-Resource IE.

[0138] As one embodiment, the first information block comprises one or more fields in a NZP-CSI-RS-ResourceSet IE.

[0139] As one embodiment, the first information block comprises one or more fields in a MeasConfig IE.

[0140] As one embodiment, the first information block comprises one or more fields in a MeasObject IE.

[0141] As one embodiment, the meaning of the first information block configuring a first measurement for mobility comprises that the first information block configures a reference signal for the first measurement for mobility.

[0142] As one embodiment, the meaning of the first information block configuring a first measurement for mobility comprises that the first information block configures a reporting of a measurement result of the first measurement for mobility.

[0143] As one embodiment, the meaning of the first information block configuring a first measurement for mobility comprises that the first information block configures a condition triggering a reporting of the first measurement for mobility.

[0144] As one embodiment, the meaning of the first information block configuring a first measurement for mobility comprises that the first information block configures an AI / ML model for the first measurement for mobility.

[0145] As one embodiment, the candidate of the first measurement comprises a measurement for BM (Beam Management).

[0146] As one embodiment, the candidate of the first measurement comprises a measurement for RLF (Radio Link Failure).

[0147] As one embodiment, the candidate of the first measurement comprises a measurement for positioning.

[0148] As one embodiment, the calculation capability unit is a Calculation Unit.

[0149] As one embodiment, the computing capability unit is a Process Unit.

[0150] As one embodiment, the computing capability unit is a Calculation Element.

[0151] As one embodiment, the computing capability unit is a Process Element.

[0152] As one embodiment, the computing capability unit is an APU.

[0153] As one embodiment, the computing capability unit is a CPU.

[0154] As one embodiment, the computing capability unit is an AI-PU.

[0155] As one embodiment, the first number is a positive integer.

[0156] As one embodiment, the number of the remaining computing capability units of the first node corresponds to the minimum computing resources required for one measurement for mobility.

[0157] As one embodiment, the first measurement for mobility is based on prediction.

[0158] As one embodiment, the first measurement for mobility is based on AI / ML generation.

[0159] As one embodiment, the first measurement for mobility is based on AI / ML model generation.

[0160] As one embodiment, the number of the remaining computing capability units of the first node refers to the number of computing capability units remaining when the first node receives the first information block configuration of the first measurement for mobility.

[0161] As one embodiment, the number of the remaining computing capability units of the first node refers to the number of computing capability units remaining before the first node processes the first measurement for mobility.

[0162] As one embodiment, the number of the remaining computing capability units of the first node refers to the number of computing capability units remaining before the first node starts the first measurement for mobility.

[0163] As one embodiment, the processing of the first measurement for mobility includes receiving a reference signal configured to the first measurement for mobility.

[0164] As one embodiment, the processing of the first measurement for mobility comprises determining a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility.

[0165] As one sub-embodiment of this embodiment, the first node implements related determining a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility.

[0166] As one sub-embodiment of this embodiment, the first node determines a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility based on baseband algorithms.

[0167] As one sub-embodiment of this embodiment, the first node determines a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility based on AI / ML models and algorithms.

[0168] As one embodiment, the processing of the first measurement for mobility comprises computing a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility.

[0169] As one sub-embodiment of this embodiment, the first node implements related computing a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility.

[0170] As one sub-embodiment of this embodiment, the first node computes a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility based on baseband algorithms.

[0171] As one sub-embodiment of this embodiment, the first node computes a measurement result of the first measurement from reception of reference signals configured to the first measurement for mobility based on AI / ML models and algorithms.

[0172] As one embodiment, the processing of the first measurement for mobility comprises predicting a reception result of reference signals configured to the first measurement for mobility and predicting a measurement result of the first measurement.

[0173] As one sub-embodiment of this embodiment, the first node implements related predicting a reception result of reference signals configured to the first measurement for mobility and predicting a measurement result of the first measurement.

[0174] As a sub-embodiment of this embodiment, the first node predicts a reception result of a reference signal configured to the first measurement for mobility based on a baseband algorithm, and predicts a measurement result of the first measurement.

[0175] As a sub-embodiment of this embodiment, the first node predicts a reception result of a reference signal configured to the first measurement for mobility based on an AI / ML model and algorithm, and predicts a measurement result of the first measurement.

[0176] As an embodiment, the first node comprises M first-type resources, and given that a first-type resource is an i-th first-type resource among the M first-type resources, a number of second-type resources included in the given first-type resource is O i , and the total number of computing unit cells of the first node is equal to wherein the value range of i is 1 to M.

[0177] As an embodiment, the first node comprises M first-type resources, and any first-type resource among the M first-type resources comprises M1 second-type resources, and the M1 is a positive integer greater than 1.

[0178] As an embodiment, the first node comprises M first-type resources, and at least two first-type resources among the M first-type resources comprise different numbers of second-type resources.

[0179] As an embodiment, the first-type resource is a processing unit of the first node.

[0180] As an embodiment, the first-type resource is an APU of the first node.

[0181] As an embodiment, the first-type resource is a CPU of the first node.

[0182] As an embodiment, the first-type resource is an AI-PU of the first node.

[0183] As an embodiment, the second-type resource included in the first-type resource is a process.

[0184] As an embodiment, the second-type resource included in the first-type resource is a storage unit.

[0185] As an embodiment, the second-type resource included in the first-type resource is a computing unit.

[0186] As an embodiment, the second-type resource included in the first-type resource is a storage unit and a computing unit.

[0187] As one embodiment, the first type of resource is an APU Set of the first node.

[0188] As one embodiment, the second type of resource included by the first type of resource is an APU.

[0189] As one embodiment, the first type of resource is a CPU Set of the first node.

[0190] As one embodiment, the second type of resource included by the first type of resource is a CPU.

[0191] As one embodiment, the first type of resource is an AI-PU Set of the first node.

[0192] As one embodiment, the second type of resource included by the first type of resource is an AI-PU.

[0193] Embodiment 2

[0194] Embodiment 2 illustrates a diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 2.

[0195] FIG. 2 illustrates a network architecture 200. The network architecture 200 is a network architecture for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architecture for LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems is referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture can be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture can be referred to as 6GS (6G System) / EPS or some other suitable terminology. The network architecture 200 can include one or more UEs 201, a RAN (Next Generation Radio Access Network) 202, a core network 210, a HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown in FIG. 2, the network architecture 200 provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application are amenable to use with networked systems providing circuit-switched services. The RAN 202 includes Node Bs 203 and other nodes 204. The Node Bs 203 provide user and control plane protocol terminations toward the UEs 201. The Node Bs 203 can be connected to the other nodes 204 via an Xn interface (e.g., backhaul). The Node Bs 203 can also be referred to as base stations, base transceiver stations, radio base stations, radio transceivers, transceiver functions, basic service sets (BSSs), extended service sets (ESSs), TRPs (Transmitter Receiver Points), or some other suitable terminology. The Node Bs 203 provide access points to the core network 210 for the UEs 201; the core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or alternatively, the core network 210 is a 6GC.Examples of a UE 201 include a cellular phone, a smart phone, a Session Initiation Protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a drone, a flying vehicle, a narrowband physical web device, a machine type communication device, a land transport vehicle, a car, a wearable device, or any other similar functional device. Those skilled in the art will also The node 203 is connected by an SI / NG interface to the core network 210. The core network 210 includes a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, a S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that processes the signaling between the UE 201 and the 5G-CN / EPC 210. The MME / AMF / SMF 211 generally provides bearer and connection management. All user Internet Protocol (IP) packets are transferred through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to the Internet services 230. The Internet services 230 include operator- correspondent Internet Protocol services, which can specifically include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched services.

[0196] As one embodiment, the first node described in this application includes the UE 201.

[0197] As one embodiment, the second node described in the present application comprises the node 203.

[0198] As one embodiment, the node 203 is a Macro Cell base station.

[0199] As one embodiment, the node 203 is a Micro Cell base station.

[0200] As one embodiment, the node 203 is a Pico Cell base station.

[0201] As one embodiment, the node 203 is a Femto Cell base station.

[0202] As one embodiment, the node 203 is a base station device supporting large latency difference.

[0203] As one embodiment, the node 203 is a flying platform device.

[0204] As one embodiment, the node 203 is a satellite device.

[0205] As one embodiment, the node 203 is a test device (e.g. a transceiver simulating part of the functions of a base station, a signaling tester).

[0206] As one embodiment, the UE 201 comprises a mobile phone.

[0207] As one embodiment, the UE 201 comprises a vehicle, including a car.

[0208] As one embodiment, the wireless link from the UE 201 to the node 203 is an uplink, which is used to perform uplink transmission.

[0209] As one embodiment, the wireless link from the node 203 to the UE 201 is a downlink, which is used to perform downlink transmission.

[0210] As one embodiment, the wireless link between the node 203 and the UE 201 comprises a cellular network link.

[0211] As one embodiment, the node 203 and the UE 201 are connected through a Uu air interface.

[0212] As one embodiment, the receiver of the first information block described in the present application comprises the UE 201.

[0213] As an embodiment, the sender of the first information block in the present application comprises the node 203.

[0214] As an embodiment, the receiver of the second information block in the present application comprises the UE 201.

[0215] As an embodiment, the sender of the second information block in the present application comprises the node 203.

[0216] As an embodiment, the sender of the first information set in the present application comprises the UE 201.

[0217] As an embodiment, the receiver of the first information set in the present application comprises the node 203.

[0218] As an embodiment, the receiver of the third information block in the present application comprises the UE 201.

[0219] As an embodiment, the sender of the third information block in the present application comprises the node 203.

[0220] As an embodiment, the UE 201 supports AI / ML model for higher layer measurement and higher layer reporting.

[0221] As an embodiment, the node 203 supports AI / ML model for higher layer measurement and higher layer reporting.

[0222] As an embodiment, the UE 201 supports 5G system.

[0223] As an embodiment, the node 203 supports 5G system.

[0224] As an embodiment, the UE 201 supports at least 6G system.

[0225] As an embodiment, the node 203 supports at least 6G system.

[0226] Embodiment 3

[0227] Embodiment 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture of user plane and control plane according to an embodiment of the present application, as shown in FIG. 3.

[0228] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3 shows the radio protocol architecture for the control plane 300 between a first communication node device (UE or RSU (Road Side Unit) in V2X (Vehicle to Everything), a vehicle mounted device or a vehicle mounted communication module) and a second node device (gNB, UE or RSU in V2X, a vehicle mounted device or a vehicle mounted communication module), or between two UEs, using three layers: Layer 1 (L1), Layer 2 (L2) and Layer 3 (L3). L1 is the lowest layer and implements various PHY (PHYsical layer) signal processing functions. L1 will be referred to as the PHY 301 herein. Layer 2 305 is above the PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs, through the PHY 301. Layer 2 305 includes a MAC (Medium Access Control) sublayer 302, a RLC (Radio Link Control) sublayer 303 and a PDCP (Packet Data Convergence Protocol) sublayer 304, which are terminated at the second node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security, by encrypting data packets, and handover support for the first communication node device between second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ (Hybrid Automatic Repeat reQuest). The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating the various radio resources (e.g., resource blocks) in one cell among the UEs. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second communication node device and the first communication node device.The radio protocol architecture of the user plane 350 includes Layer 1 (L1) and Layer 2 (L2), which are substantially the same as the corresponding layers and sublayers in the control plane 300 for the first communication node device and the second communication node device, for the physical layer 351, the PDCP sublayer 354 in L2 355, the RLC sublayer 353 in L2 355, and the MAC sublayer 352 in L2 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead. Also included in L2 355 in the user plane 350 is the SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS (Quality of Service) flows and data radio bearers (DRBs) to support diverse traffic types. Although not illustrated, the first communication node device can have several upper layers above L2 355, including a network layer (e.g., IP (Internet Protocol) layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0229] As one embodiment, the radio protocol architecture in FIG. 3 is applicable to the first node in the present application.

[0230] As one embodiment, the radio protocol architecture in FIG. 3 is applicable to the second node in the present application.

[0231] As one embodiment, the first information block is generated at the RRC 306.

[0232] As one embodiment, the first information block is generated at the MAC 302 or the MAC 352.

[0233] As one embodiment, the second information block is generated at the RRC 306.

[0234] As one embodiment, the second information block is generated at the MAC 302 or the MAC 352.

[0235] As one embodiment, the first information set is generated at the RRC 306.

[0236] As one embodiment, the first information set is generated at the MAC 302 or the MAC 352.

[0237] As one embodiment, the third information block is generated at the RRC 306.

[0238] As one embodiment, the third information block is generated at the MAC 302 or the MAC 352.

[0239] Embodiment 4

[0240] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application, as shown in FIG. 4. FIG. 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0241] The first communication device 410 includes a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multiple antenna receive processor 472, a multiple antenna transmit processor 471, a transmitter / receiver 418, and an antenna 420.

[0242] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multiple antenna transmit processor 457, a multiple antenna receive processor 458, a transmitter / receiver 454, and an antenna 452.

[0243] In transmissions from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements functionality of L2. In DL, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations for second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for Ll (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450 and mapping onto signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial pre-coding of the coded and modulated symbols, including codebook-based and non-codebook-based pre-coding and beamforming processing, to generate one or more parallel streams. The transmit processor 416 then maps to each of the parallel streams to subcarriers, multiplexes the modulated symbols in time domain and / or frequency domain with reference signals (e.g., pilot) and then performs an inverse fast Fourier transform (IFFT) to generate time domain multicarrier symbol streams. The multi-antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time domain multicarrier symbol streams. Each transmitter 418 converts the baseband multicarrier symbol streams provided by the multi-antenna transmit processor 471 into radio frequency signals that are transmitted via the corresponding antennas 420.

[0244] In transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and converts the RF stream into a baseband, multicarrier symbol stream to receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions of the LI. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operation on the baseband, multicarrier symbol stream from the receivers 454. The receive processor 456 converts the baseband, multicarrier symbol stream from the receive analog precoding / beamforming operation from the time domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signals and the reference signals are demultiplexed by the receive processor 456, where the reference signals will be used for channel estimation, and the data signals are recovered after multi-antenna detection in the multi-antenna receive processor 458 for any parallel streams destined to the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456 and generate soft decisions. The receive processor 456 then decodes and de-interleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channels. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2. The controller / processor 459 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer-readable medium. In the DL, the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2. Various control signals can also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using an ACK and / or negative ACK (NACK) protocol to support HARQ operations.

[0245] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer packets to a controller / processor 459. The data source 467 represents all protocol layers above L2. Similar to the transmit function described at the first communication device 410 in the DL, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations for the first communication device 410, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468, in conjunction with a multi-antenna transmit processor 457, performs modulation mapping, channel coding processing, digital multi-antenna spatial pre-coding including codebook-based and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 generates parallel streams of symbols that are modulated onto different carriers, and the modulated symbol streams are then provided to different antennas 452 via transmitters 454 after analog pre-coding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 converts a baseband symbol stream into a radio frequency signal that is transmitted via the corresponding antenna 452.

[0246] In the transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the functionality described in connection with the reception at the second communication device 450 in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a radio frequency signal through its respective antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 together implement L1 layer functionality. A controller / processor 475 implements L2 layer functionality. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer packets from the second communication device 450. Upper layer packets from the controller / processor 475 can be provided to a core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0247] As one embodiment, the second communication device 450 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the second communication device 450 to perform at least the following: receive a first information block, the first information block being configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility being equal to a first value; process the first measurement for mobility only when the first value is not greater than a number of remaining computing capability units of a first node; the number of remaining computing capability units of the first node being equal to a total number of computing capability units of the first node minus a number of computing capability units already occupied; the total number of computing capability units of the first node depending on a number of first type resources included in the first node, and a number of second type resources included in a corresponding first type resource; at least one second type resource being included in any one first type resource included in the first node.

[0248] As one embodiment, the second communication device 450 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes the second communication device 450 to perform at least the following: receive a first information block, the first information block being configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility being equal to a first value; process the first measurement for mobility only when the first value is not greater than a number of remaining computing capability units of a first node.

[0249] As one embodiment, the first communication device 410 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the first communication device 410 to perform at least the following: send a first information block, the first information block being configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility being equal to a first value; a receiver of the first information block comprising a first node; the first node processing the first measurement for mobility only when the first value is not greater than a number of remaining computing capability units of the first node; the number of remaining computing capability units of the first node being equal to a total number of computing capability units of the first node minus a number of computing capability units already occupied; the total number of computing capability units of the first node depending on a number of first type resources included in the first node, and a number of second type resources included in a corresponding first type resource; at least one second type resource being included in any one first type resource included in the first node.

[0250] As one embodiment, the first communication device 410 comprises a memory that stores a computer readable program, which, when executed by at least one processor, causes actions comprising: transmitting a first information block, the first information block being configured for a first measurement for mobility, a number of computational capability units occupied by the first measurement for mobility being equal to a first value.

[0251] As one embodiment, the first node in the present application comprises the second communication device 450.

[0252] As one embodiment, the second node in the present application comprises the first communication device 410.

[0253] As one embodiment, at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the first information block in the present application; at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to receive the first information block in the present application.

[0254] As one embodiment, at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to process the first measurement for mobility only when the first value is not greater than a number of remaining computational capability units of the first node.

[0255] As one embodiment, at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the second information block in the present application; at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to receive the second information block in the present application.

[0256] As an embodiment, at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the third information block; at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to receive the third information block.

[0257] As an embodiment, at least one of {the antenna 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is configured to transmit the first information set; at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} is configured to receive the first information set.

[0258] Embodiment 5

[0259] Embodiment 5 illustrates a flow chart of the transmission between the first node and the second node according to an embodiment of the present application, as shown in FIG. 5. In FIG. 5, the first node U1 communicates with the second node N2 through a wireless link, and the steps in block F50 are optional. It is particularly pointed out that the order in this embodiment does not limit the order of signal transmission and implementation in the present application.

[0260] For the first node U1, the first information set is transmitted in step S510; the first information block is received in step S511; and the first measurement for mobility is processed in step S512.

[0261] For the second node N2, the first information set is received in step S520; and the first information block is transmitted in step S521.

[0262] In embodiment 5, the first information block configures the first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility is equal to a first value; the first node processes the first measurement for mobility only when the first value is not greater than a number of remaining computing capability units of the first node; the number of remaining computing capability units of the first node is equal to a total number of computing capability units of the first node minus a number of computing capability units already occupied; the total number of computing capability units of the first node depends on a number of first-type resources included in the first node, and a number of second-type resources included in the corresponding first-type resources; at least one second-type resource is included in any one first-type resource included in the first node; the first information set indicates the number of first-type resources included in the first node, and the number of second-type resources included in the corresponding first-type resources.

[0263] As an embodiment, the first node U1 is the first node in the present application.

[0264] As an embodiment, the second node N2 is the second node in the present application.

[0265] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a base station device and a user equipment.

[0266] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a relay node device and a user equipment.

[0267] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a user equipment and a user equipment.

[0268] As an embodiment, the second node N2 and the first node U1 communicate through a Uu interface.

[0269] As an embodiment, the second node N2 is a serving cell maintenance base station of the first node U1.

[0270] As an embodiment, the step S512 further includes receiving a reference signal configured to the first measurement.

[0271] As an embodiment, the step S512 further includes generating a measurement report according to a measurement result of the first measurement.

[0272] As an embodiment, the step S512 further includes sending a measurement report generated according to a measurement result of the first measurement.

[0273] As an embodiment, the step S512 further comprises determining that the first value is not greater than a remaining number of computing capability units of the first node.

[0274] As an embodiment, the step S521 further comprises determining that the first value is not greater than a remaining number of computing capability units of the first node.

[0275] As an embodiment, the step S521 further comprises receiving a measurement report generated according to a measurement result of the first measurement.

[0276] As an embodiment, the first information set comprises an RRC IE.

[0277] As an embodiment, the first information set belongs to capability information of the first node.

[0278] As an embodiment, the first information set comprises capability information of the first node.

[0279] As an embodiment, the first information set comprises one or more capability parameters of the first node.

[0280] As an embodiment, the first information set comprises one or more fields in one or more UE(user equipment) capability IE(information element).

[0281] As an embodiment, the first information set comprises one or more parameters in one or more UE(user equipment) capability IE.

[0282] As an embodiment, the first node transmits capability information of the first node after receiving a UE capability enquiry from a network, and the first information set belongs to the capability information of the first node.

[0283] As an embodiment, the capability information of the first node comprises UECapabilityInformation.

[0284] As an embodiment, the capability information of the first node comprises radio access capability of the first node.

[0285] Typically, the first type of resource corresponds to a processing unit included in the first node, and the second type of resource corresponds to a process supported by one processing unit included in the first node; and the total number of computing capability units of the first node corresponds to a total number of processes supported by all processing units included in the first node.

[0286] Typically, the first value depends on an ID of a first model used for the first measurement for mobility, and the first model is based on AI / ML.

[0287] As an embodiment, the first value varies according to the ID of different models.

[0288] As an embodiment, the ID of the first model is used to determine the first value.

[0289] As an embodiment, the first model is one of K1 candidate models, the K1 candidate models respectively correspond to K1 candidate values, the first value is a candidate value corresponding to the first model among the K1 candidate values; and the K1 is a positive integer greater than 1.

[0290] As a sub-embodiment of this embodiment, any candidate value among the K1 candidate values is a positive integer.

[0291] As an embodiment, the ID of the first model includes a Model ID.

[0292] As an embodiment, the ID of the first model includes a Functionality ID.

[0293] As an embodiment, the first information block configures the first model.

[0294] As an embodiment, the first information block indicates the first model used for the first measurement for mobility.

[0295] As an embodiment, the first model being based on AI / ML means that the first model is used for AI / ML.

[0296] As an embodiment, the first model being based on AI / ML means that the first model is used for prediction.

[0297] Typically, the first value is a positive integer greater than 1, and the first node determines to process the first measurement for mobility; and the first node does not assume that computing capability units occupied by the first measurement for mobility are located in two different first types of resources.

[0298] As one embodiment, the first node assumes that the computing capacity units occupied by the first measurements for mobility are located in a first type of resource.

[0299] As one embodiment, for a given first type of resource, when the number of the second type of resources remaining for the given first type of resource is less than the first number, the given first type of resource is not occupied by the first measurements.

[0300] Embodiment 6

[0301] Embodiment 6 illustrates a flowchart of the transmission between the first node and the second node according to another embodiment of the present application, as shown in FIG. 6. In FIG. 6, the first node U3 communicates with the second node N4 through a wireless link. It is particularly pointed out that the sequence in this embodiment does not limit the sequence of signal transmission and implementation in the present application.

[0302] For the first node U3, the second information block is received in step S530.

[0303] For the second node N4, the second information block is transmitted in step S540.

[0304] In embodiment 6, the second information block configures the second measurements for mobility, the number of computing capacity units occupied by the second measurements for mobility is equal to a second number; the priority targeted by the second measurements is lower than the priority targeted by the first measurements; the first number is not greater than the number of remaining computing capacity units of the first node; the second number is not greater than the number of remaining computing capacity units of the first node; the sum of the first number and the second number is greater than the number of remaining computing capacity units of the first node; the second measurements for mobility are not processed.

[0305] As one embodiment, the step S530 is followed by a step of determining that the second measurements for mobility are not processed.

[0306] As one embodiment, the step S530 is followed by a step of abandoning the second measurements for mobility.

[0307] As one embodiment, the step S540 is followed by a step of determining that the second measurements for mobility are not processed.

[0308] As one embodiment, the second information block is transmitted through RRC signaling.

[0309] As one embodiment, the second information block includes one or more RRC IEs.

[0310] As one embodiment, the second information block includes one or more fields in a ServingCellConfig IE.

[0311] As one embodiment, the name of the RRC signaling used to transmit the second information block includes CSI.

[0312] As one embodiment, the name of the RRC signaling used to transmit the second information block includes CSI-RS.

[0313] As one embodiment, the name of the RRC signaling used to transmit the second information block includes Report.

[0314] As one embodiment, the name of the RRC signaling used to transmit the second information block includes Config.

[0315] As one embodiment, the name of the RRC signaling used to transmit the second information block includes Meas.

[0316] As one embodiment, the name of the RRC signaling used to transmit the second information block includes Measurement.

[0317] As one embodiment, the name of the RRC signaling used to transmit the second information block includes Object.

[0318] As one embodiment, the second information block includes a CSI-ReportConfig IE.

[0319] As one embodiment, the second information block includes a CSI-ReportSubConfig IE.

[0320] As one embodiment, the second information block includes a ServingCellConfig IE.

[0321] As one embodiment, the second information block includes one or more fields in a ServingCellConfig IE.

[0322] As one embodiment, the second information block includes a CSI-MeasConfig IE.

[0323] As one embodiment, the second information block includes one or more fields in a CSI-MeasConfig IE.

[0324] As one embodiment, the second information block includes a CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0325] As one example, the second information block includes one or more fields in the CSI-SemiPersistentOnPUSCH-TriggerStateList IE.

[0326] As one embodiment, the second information block includes CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0327] As one embodiment, the second information block includes one or more fields from CSI-SemiPersistentOnPUSCH-TriggerStateIE.

[0328] As one embodiment, the second information block includes a CSI-ReportSubConfgTriggerList IE.

[0329] As one example, the second information block includes one or more fields in the CSI-ReportSubConfigTriggerList IE.

[0330] As one embodiment, the second information block includes CSI-ReportConfig IE.

[0331] As one example, the second information block includes one or more fields in CSI-ReportConfigIE.

[0332] As one embodiment, the second information block includes CSI-ReportSubConfig IE.

[0333] As one example, the second information block includes one or more fields in the CSI-ReportSubConfig IE.

[0334] As one example, the second information block includes one or more fields in the CSI-AperiodicTriggerStateList IE.

[0335] As one example, the second information block includes one or more fields in the CSI-IM-Resource IE.

[0336] As one embodiment, the second information block includes one or more fields in the CSI-IM-ResourceSet IE.

[0337] As one example, the second information block includes one or more domains in the CSI-ResourceConfig IE.

[0338] As one embodiment, the second information block comprises one or more fields in the CSI-RS-ResourceConfigMobility IE.

[0339] As one embodiment, the second information block comprises one or more fields in the NZP-CSI-RS-Resource IE.

[0340] As one embodiment, the second information block comprises one or more fields in the NZP-CSI-RS-ResourceSet IE.

[0341] As one embodiment, the second information block comprises one or more fields in the MeasConfig IE.

[0342] As one embodiment, the second information block comprises one or more fields in the MeasObject IE.

[0343] As one embodiment, the meaning of the second information block configuring the second measurement for mobility comprises that the second information block configures a reference signal for the second measurement for mobility.

[0344] As one embodiment, the meaning of the second information block configuring the second measurement for mobility comprises that the second information block configures a reporting of a measurement result of the second measurement for mobility.

[0345] As one embodiment, the meaning of the second information block configuring the second measurement for mobility comprises that the second information block configures a condition triggering a reporting of the second measurement for mobility.

[0346] As one embodiment, the meaning of the second information block configuring the second measurement for mobility comprises that the second information block configures an AI / ML model for the second measurement for mobility.

[0347] As one embodiment, the first information block and the second information block are two RRC IEs respectively.

[0348] As one embodiment, the first information block and the second information block are two fields in one RRC IE respectively.

[0349] As one embodiment, the first measurement and the second measurement are different.

[0350] As one sub-embodiment of this embodiment, the first measurement and the second measurement are respectively for different Objects.

[0351] As one subembodiment of the embodiment, the first measurement and the second measurement employ different reference signal resources.

[0352] As one subembodiment of the embodiment, the first measurement and the second measurement are respectively for different frequency bands.

[0353] As one subembodiment of the embodiment, the first measurement and the second measurement are respectively for different purposes.

[0354] As one subembodiment of the embodiment, the first measurement and the second measurement are respectively for different cells.

[0355] As one subembodiment of the embodiment, the first measurement and the second measurement are respectively for different RATs.

[0356] As one embodiment, the second value is a positive integer.

[0357] As one embodiment, the meaning that the priority for the second measurement is lower than the priority for the first measurement includes that, in case of resource limitation, the first measurement is processed preferentially compared to the second measurement.

[0358] As one embodiment, the meaning that the priority for the second measurement is lower than the priority for the first measurement includes that, in case of resource limitation, the second measurement is dropped and the first measurement is kept.

[0359] As one embodiment, the meaning that the priority for the second measurement is lower than the priority for the first measurement includes that the value of the priority for the second measurement is greater than the value of the priority for the first measurement.

[0360] As one embodiment, the meaning that the priority for the second measurement is lower than the priority for the first measurement includes that the value of the priority for the second measurement is less than the value of the priority for the first measurement.

[0361] As one embodiment, the priority for the first measurement is configured by higher layer signaling.

[0362] As one embodiment, the priority for the second measurement is configured by higher layer signaling.

[0363] As one embodiment, the priority for the first measurement depends on the measurement type of the first measurement.

[0364] As one embodiment, the priority for the first measurement depends on the measurement target of the first measurement.

[0365] As one embodiment, the priority of the second measurement depends on a frequency band of the first measurement.

[0366] As one embodiment, the priority of the second measurement depends on a measurement type of the first measurement.

[0367] As one embodiment, the priority of the second measurement depends on a measurement target of the first measurement.

[0368] As one embodiment, the priority of the second measurement depends on a frequency band of the first measurement.

[0369] As one embodiment, the second measurement for mobility is not processed means that the second measurement is dropped.

[0370] As one embodiment, the step S530 is not earlier than the step S511 in Embodiment 5.

[0371] As one embodiment, the step S540 is not earlier than the step S521 in Embodiment 5.

[0372] Embodiment 7

[0373] Embodiment 7 illustrates a flowchart of transmission between a first node and a second node according to yet another embodiment of the present application, as shown in FIG. 7. In FIG. 7, the first node U5 communicates with the second node N6 through a wireless link. It is particularly pointed out that the sequence in this embodiment does not limit the sequence of signal transmission and the sequence of implementation in the present application.

[0374] For the first node U5, a third information block is received in step S550.

[0375] For the second node N6, the third information block is transmitted in step S560.

[0376] In Embodiment 7, the first value depends on an indication of the third information block.

[0377] As one embodiment, the third information block includes an RRC IE.

[0378] As one embodiment, the third information block includes one or more fields in an RRC IE.

[0379] As one embodiment, the third information block configures the first model.

[0380] As one embodiment, the third information block indicates an ID of the first model.

[0381] As one embodiment, the third information block indicates the first value.

[0382] As one embodiment, the ID of the first model indicated by the third information block is used to determine the first value.

[0383] As one embodiment, the first value depends on the ID of the first model indicated by the third information block.

[0384] As one embodiment, the step S550 is after the step S511 in embodiment 5.

[0385] As one embodiment, the step S550 is before the step S511 in embodiment 5.

[0386] As one embodiment, the step S560 is after the step S521 in embodiment 5.

[0387] As one embodiment, the step S560 is before the step S521 in embodiment 5.

[0388] Embodiment 8

[0389] Embodiment 8 illustrates a schematic diagram of the first type of resources and the second type of resources according to one embodiment of the present application, as shown in FIG. 8. In FIG. 8, the first node includes M first type of resources, where M is a positive integer greater than 1; any of the M first type of resources includes at least one second type of resource.

[0390] As one embodiment, the total number of all the second type of resources included by the first node in the M first type of resources corresponds to the number of maximum computing power of the first node.

[0391] As one embodiment, the number of the first type of resources included by the first node corresponds to the number of simultaneously enabled AI / ML models supported by the first node.

[0392] As one embodiment, the number of the first type of resources included by the first node corresponds to the number of simultaneously enabled AI / ML functionality supported by the first node.

[0393] As one embodiment, the number of the first type of resources included by the first node corresponds to the number of simultaneously enabled AI / ML computing modules supported by the first node.

[0394] Embodiment 9

[0395] Embodiment 9 illustrates a diagram of a first measurement according to an embodiment of the present application, as shown in FIG. 9. In FIG. 9, the first measurement is one of a plurality of candidate measurements, and the plurality of candidate measurements are all for higher layer measurement.

[0396] As an embodiment, at least one of the plurality of candidate measurements is for mobility.

[0397] As an embodiment, at least one of the plurality of candidate measurements is for RLM (Radio Link Monitoring).

[0398] As an embodiment, at least one of the plurality of candidate measurements is for RRM (Radio Resource Management).

[0399] As an embodiment, the second measurement in the present application is one of the plurality of candidate measurements.

[0400] As an embodiment, the plurality of candidate measurements are all for a UE in connected state.

[0401] As an embodiment, the plurality of candidate measurements are all for a UE in idle state.

[0402] Embodiment 10

[0403] Embodiment 10 illustrates a diagram of a first type of resource occupation according to an embodiment of the present application, as shown in FIG. 10. In FIG. 10, the second type of resource filled with diagonal lines is not occupied, and the second type of resource not filled with diagonal lines is occupied.

[0404] In embodiment 10, the first node includes first type of resource #1 and first type of resource #2, the first type of resource #1 includes only one second type of resource not occupied, and the first type of resource #2 includes no less than two second type of resources; the first value is no more than the remaining computing capability unit number of the first node, and the first value is equal to 2; the remaining second type of resource in the first type of resource #1 is not occupied by the first measurement, and the remaining second type of resource in the first type of resource #2 is occupied by the first measurement.

[0405] As an embodiment, the above description is a non-limiting description of the following features:

[0406] The first value is a positive integer greater than 1, the first node determines to process the first measurement for mobility; the first node does not assume that the computing capability unit occupied by the first measurement for mobility is located in two different first type of resources.

[0407] Embodiment 11

[0408] Embodiment 11 illustrates a schematic diagram of RAN-domain AI / ML function deployment according to an embodiment of the present application, as shown in FIG. 11. In FIG. 11, gNB can be replaced by eNB, or 6G base station, or other network equipment.

[0409] In Embodiment 11, the management of ML inference functions of multiple base stations is done by RAN-domain management function 1102, i.e., data interaction with RAN-domain MnS (Management Service) consumer / cross-domain management 1101 (as shown by the dashed arrow in FIG. 11). RAN-domain ML training function 1103 is located in RAN-domain management function 1102; while ML inference functions are located in base stations, i.e., AI / ML inference function 1104 is located in gNB 1105, AI / ML inference function 1106 is located in gNB 1107, and so on.

[0410] AI / ML related functions include ML training function (also referred to as AI training, or AI / ML training), ML testing function, ML inference function (also referred to as AI inference, or AI / ML inference), and so on. ML training function, ML testing function, ML inference function can be deployed independently, or co-located. The deployment of AI / ML related functions can be implemented by software, such as the download and / or running of executable files; or implemented by software combined with hardware, such as specific computing units accelerated by hardware to improve operation speed or save power consumption.

[0411] For ML training function, it can be deployed in cross-domain management system, or domain-specific management system for managing RAN domain or CN (Core Network) domain. For example, for MDA (Management Data Analytics) ML training function, it can be deployed in MDAF (Management Data Analytic Function); for network data analytics ML training, it can be deployed in NWDAF (NetWork Data Analytics Function), i.e., ML training function is MTLF (Model Training Logical Function).

[0412] For the ML inference function, it can also be deployed in the cross-domain management system or the domain-specific management system; for example, the ML inference function is the MDAF, or the ML inference function is the AnLF (Analytics Logical Function) located in the NWDAF.

[0413] Similarly, the ML test function can also be deployed in the cross-domain management system or the domain-specific management system.

[0414] Optionally, the management of the ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1101 for data.

[0415] It should be noted that embodiment 11 is only one non-limiting implementation; optionally, the ML training function of the RAN domain can also be deployed in the base station; or optionally, part of the base stations deploy the ML inference function and the ML training function of the RAN domain, and part of the base stations only deploy the ML inference function.

[0416] As an embodiment, one gNB (or base station) in embodiment 11 is the second node of the application.

[0417] Embodiment 12

[0418] Embodiment 12 illustrates a schematic diagram of AI / ML function deployment of a UE according to an embodiment of the application, as shown in FIG. 12. In FIG. 12, the RAN domain ML training function 1204 is optional.

[0419] The UE function 1203 is deployed in the first node of the application, and the UE function 1203 includes an AI / ML inference function 1205; the AI / ML inference function 1205 uses a ML model (also referred to as an AI model) for inference; a ML model is usually trained before being used for AI / ML inference.

[0420] As an embodiment, the UE function 1203 includes the RAN domain ML training function 1204, which runs training data through a ML model to derive a related loss, and adjusts parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0421] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments have higher requirements for the processing capability of the UE side.

[0422] Optionally, the UE function 1203 further comprises a CN domain ML training function (not included in FIG. 12).

[0423] Optionally, the UE function 1203 further comprises an AI / ML deployment function (not included in FIG. 12) for loading ML models and data.

[0424] As an embodiment, the first node indicates whether the ML training function (RAN domain or CN domain) is supported through capability reporting, and the capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0425] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0426] Optionally, the UE function 1203 is an MnS producer that provides data to the CN domain MnF and / or the RAN domain MnF and / or the cross-domain management system 1201 for management or analysis (as indicated by the double-headed arrow 1202).

[0427] Optionally, the UE function 1203 is an MnS consumer that loads data from the CN domain MnF and / or the RAN domain MnF and / or the cross-domain management system 1201 for AI / ML-related management, such as management data requests, ML model activation, and / or ML training, etc. (as indicated by the double-headed arrow 1202).

[0428] As an embodiment, the second information block in the present application is obtained through inference of the AI / ML inference function 1105.

[0429] As an embodiment, the reporting information of the beam management in the present application is obtained through inference of the AI / ML inference function 1105.

[0430] As an embodiment, the AI / ML model is based on NN.

[0431] As an embodiment, the AI / ML model is based on ANN.

[0432] As one embodiment, the AI / ML model is based on a CNN.

[0433] As one embodiment, the AI / ML model is based on a LLM architecture.

[0434] As one embodiment, the AI / ML model is based on a Transformer architecture.

[0435] As one embodiment, the AI / ML model is based on an LSTM.

[0436] As one embodiment, the AI / ML model is based on an MLP.

[0437] As one embodiment, the AI / ML model is based on a GAN.

[0438] As one embodiment, the AI / ML model is based on a light-weight neural network.

[0439] As one sub-embodiment of this embodiment, the light-weight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0440] Embodiment 13

[0441] Embodiment 13 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to one embodiment of the present application, as shown in FIG. 13. In FIG. 13, the artificial intelligence or machine learning based processing system includes a first processing machine, a second processing machine, a third processing machine, and a fourth processing machine.

[0442] In Embodiment 13, the first processing machine sends a first data set to the second processing machine, and a second data set to the third processing machine; the second processing machine generates a target first-class parameter group according to the first data set, and sends the generated target first-class parameter group to the third processing machine; the third processing machine processes the second data set using the target first-class parameter group to obtain a first-class output, and optionally, sends the first-class output to the fourth processing machine. In FIG. 13, a first-class feedback and a second-class feedback are optional; the second processing machine includes an ML training function; and the third processing machine includes an ML inference function.

[0443] As one embodiment, the fourth processing machine includes an ML testing function.

[0444] As one embodiment, the fourth processing machine includes performance monitoring / evaluation of the ML model.

[0445] As an embodiment, the third processor sends first type feedback to the second processor; the first type feedback is used to trigger re-computation or update of the target first type parameter group, i.e. trigger ML initial training or ML re-training.

[0446] As an embodiment, the fourth processor sends second type feedback to the first processor; the second type feedback is used to generate the first data set or the second data set, or the second type feedback is used to trigger sending of the first data set or sending of the second data set.

[0447] As an embodiment, the first processor generates the first data set and the second data set according to measurement of a reference signal.

[0448] As an embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0449] As an embodiment, the first data set includes training data.

[0450] As an embodiment, the second processor is used to train an ML model, and the trained model is described by the target first type parameter group.

[0451] As an embodiment, the second processor belongs to the first node; the above method avoids passing the first data set to the second node.

[0452] As an embodiment, the second processor belongs to the second node; the above method supports joint training and optimizes system performance.

[0453] As an embodiment, the second processor belongs to a core network; the above method supports network-wide joint training and further optimizes system performance.

[0454] As an embodiment, the second data set includes inference data.

[0455] As an embodiment, the third processor belongs to the first node.

[0456] As an embodiment, the third processor constructs a model according to the target first type parameter group, and then inputs the second data set into the constructed model to obtain the first type output.

[0457] As an embodiment, the second data set includes the first reference signal.

[0458] As an embodiment, the second data set includes L1 measurement results obtained by measuring the first reference signal.

[0459] As an embodiment, the second data set comprises beam level measurement results obtained by measuring the first reference signal.

[0460] As an embodiment, the second data set comprises L1 prediction results generated by the first node based on the first reference signal.

[0461] As an embodiment, the second data set comprises cell level prediction results generated by the first node based on the first reference signal.

[0462] As an embodiment, the first type of output comprises the second information block.

[0463] As an embodiment, the first type of output comprises the reporting information of the beam management.

[0464] As an embodiment, the first type of output comprises whether to send the first information block.

[0465] As an embodiment, the first type of output comprises the first threshold.

[0466] As an embodiment, the first type of output comprises the second threshold.

[0467] As an embodiment, the first type of output comprises a time predicted by the first node at which the first information block is triggered based on the first reference signal.

[0468] As an embodiment, the first type of output comprises a time predicted by the first node at which RLF occurs based on the first reference signal.

[0469] As an embodiment, the third processor generates a recovery data set according to the first type of output, and an error of the recovery data set and the second data set is used to generate the first type of feedback.

[0470] As an embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model cannot meet the requirements, the second processor recalculates the target first type of parameter group.

[0471] As an embodiment, the performance of the trained model is considered to be unable to meet the requirements when the error is too large or the time for updating is too long.

[0472] As an embodiment, the target first type of parameter group comprises one or more of a convolution kernel, a pool core, a pooling function, an activation function, a parameter of the pooling function, or a parameter of the activation function.

[0473] As an example, the target first-type parameter group comprises one or more of a convolution kernel size, a convolution layer number, a convolution stride, a pooling kernel size, a pooling kernel stride, a pooling function, an activation function, or a feature map number.

[0474] Embodiment 14

[0475] Embodiment 14 illustrates an AI / ML based schematic diagram according to an embodiment of the present application, as shown in FIG. 14. In FIG. 14, the first operation and the second operation belong to a first stage, the third operation belongs to a second stage, the fourth operation belongs to a third stage, and the fifth operation belongs to a fourth stage; the arrowed line represents the order of the flow.

[0476] As an example, the first operation comprises AI / ML training, the second operation comprises AI / ML testing, the third operation comprises AI / ML emulation, the fourth operation comprises AI / ML entity loading, and the fifth operation comprises AI / ML inference.

[0477] As an example, the first stage comprises a training phase, the second stage comprises an emulation phase, the third stage comprises a deployment phase, and the fourth stage comprises an inference phase.

[0478] As an example, the first stage comprises AI / ML model training.

[0479] As an example, the first stage comprises AI / ML model training and AI / ML testing.

[0480] As an example, the AI / ML model training comprises initial training and re-training of one or a group of AI / ML entities.

[0481] As an example, the AI / ML model training relies on training data.

[0482] As an example, the AI / ML model training comprises AI / ML entity validation.

[0483] As one embodiment, the AI / ML entity validation is used to evaluate the performance of the AI / ML entity.

[0484] As one embodiment, the AI / ML entity validation relies on validation data.

[0485] As one embodiment, if the result of the AI / ML entity validation does not meet the expectation, the AI / ML model will be retrained.

[0486] As one embodiment, the AI / ML testing includes testing the validated AI / ML entity to evaluate the performance of the trained AI / ML model.

[0487] As one embodiment, if the result of the AI / ML testing meets the expectation, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[0488] As one embodiment, the AI / ML testing relies on testing data.

[0489] As one embodiment, the second stage includes AI / ML simulation, which simulates the inference of the AI / ML entity in a simulation environment.

[0490] As one embodiment, the AI / ML simulation is to evaluate the performance of the inference of the AI / ML entity in a simulation environment before the AI / ML entity is used.

[0491] As one embodiment, the second stage is optional.

[0492] As one embodiment, the third stage includes AI / ML entity loading, which is to obtain the trained AI / ML entity to obtain the desired AI / ML inference function.

[0493] As one embodiment, the third stage is optional.

[0494] As one embodiment, the third stage is not needed when the training function and the inference function are co-located.

[0495] As one embodiment, the fourth stage includes AI / ML inference.

[0496] Embodiment 15

[0497] Embodiment 15 illustrates a block diagram of a structure of a processing device in a first node according to one embodiment of the present application, as shown in FIG. 15. In FIG. 15, the processing device 1500 in the first node includes a first receiver 1501 and a first transmitter 1502.

[0498] In embodiment 15, the first receiver 1501 receives a first information block, the first information block is configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility is equal to a first value;

[0499] The first receiver 1501 processes the first measurement for mobility only when the first value is not greater than a number of remaining computing capability units of the first node;

[0500] In embodiment 15, the number of remaining computing capability units of the first node is equal to a total number of computing capability units of the first node minus a number of computing capability units that have been occupied; the total number of computing capability units of the first node depends on a number of first-type resources included in the first node, and a number of second-type resources included in the corresponding first-type resources; any one first-type resource included in the first node includes at least one second-type resource.

[0501] As an embodiment, the first-type resources correspond to processing units included in the terminal, and the second-type resources correspond to a process supported by one processing unit included in the terminal; the total number of computing capability units of the terminal corresponds to a total number of processes supported by all processing units included in the terminal.

[0502] As an embodiment, the first value depends on an ID of a first model, the first model is used for the first measurement for mobility, and the first model is based on AI / ML.

[0503] As an embodiment, the first receiver 1501 receives a second information block, the second information block is configured for a second measurement for mobility, a number of computing capability units occupied by the second measurement for mobility is equal to a second value; a priority to which the second measurement is directed is lower than a priority to which the first measurement is directed; the first value is not greater than the number of remaining computing capability units of the terminal; the second value is not greater than the number of remaining computing capability units of the terminal; a sum of the first value and the second value is greater than the number of remaining computing capability units of the terminal; and the second measurement for mobility is not processed.

[0504] As an embodiment, the first measurement for mobility is one measurement in a first-type measurement set, and the first-type measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

[0505] As one embodiment, the first measurement for mobility is one measurement in a second set of measurements, the second set of measurements comprising at least one of cell selection, cell reselection, and cell handover.

[0506] As one embodiment, the first number is a positive integer greater than 1, and the terminal determines to process the first measurement for mobility; the terminal does not assume that the computing capability units occupied by the first measurement for mobility are located in two different first type resources.

[0507] As one embodiment, the first transmitter 1502 transmits a first information set; the first information set indicates the number of first type resources included by the terminal, and the number of second type resources included by the corresponding first type resources.

[0508] As one embodiment, the first receiver 1501 receives a second information block; the first number depends on the indication of the second information block.

[0509] As one embodiment, the first node 1500 is a user equipment.

[0510] As one embodiment, the first node 1500 is a terminal.

[0511] As one embodiment, the first node 1500 is a relay node device.

[0512] As one embodiment, the first receiver 1501 comprises at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} in embodiment 4.

[0513] As one embodiment, the first transmitter 1502 comprises at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, the data source 467} in embodiment 4.

[0514] Embodiment 16

[0515] Embodiment 16 illustrates a structural block diagram of a processing apparatus in a second node according to one embodiment of the present application, as shown in FIG. 16. In FIG. 16, the processing apparatus 1600 in the second node comprises a second transmitter 1601 and a second receiver 1602.

[0516] In embodiment 16, the second transmitter 1601 transmits a first information block, the first information block is configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility is equal to a first value;

[0517] In embodiment 16, a receiver of the first information block includes a first node; only when the first value is not greater than a number of remaining computing capability units of the first node, the first node processes the first measurement for mobility; the number of remaining computing capability units of the first node is equal to a total number of computing capability units of the first node minus a number of computing capability units that have been occupied; the total number of computing capability units of the first node depends on a number of first-type resources included by the first node, and a number of second-type resources included by corresponding first-type resources; at least one second-type resource is included in any one first-type resource included by the first node.

[0518] As an embodiment, the first-type resources correspond to processing units included by the first node, the second-type resources correspond to a process supported by one processing unit included by the first node; the total number of computing capability units of the first node corresponds to a total number of processes supported by all processing units included by the first node.

[0519] As an embodiment, the first value depends on an ID of a first model, the first model is used for the first measurement for mobility, the first model is based on AI / ML.

[0520] As an embodiment, the second transmitter 1601 transmits a second information block, the second information block is configured for a second measurement for mobility, a number of computing capability units occupied by the second measurement for mobility is equal to a second value; a priority to which the second measurement is directed is lower than a priority to which the first measurement is directed; the first value is not greater than the number of remaining computing capability units of the first node; the second value is not greater than the number of remaining computing capability units of the first node; a sum of the first value and the second value is greater than the number of remaining computing capability units of the first node; the second measurement for mobility is not processed by the first node.

[0521] As an embodiment, the first measurement for mobility is one measurement in a first-type measurement set, the first-type measurement set includes at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

[0522] As one embodiment, the first measurement for mobility is one measurement in a second set of measurements, the second set of measurements comprising at least one of cell selection, cell reselection, cell handover.

[0523] As one embodiment, the first number is a positive integer greater than 1, the first node determines to process the first measurement for mobility; the first node does not assume that a computing capability unit occupied by the first measurement for mobility is located in two different first type resources.

[0524] As one embodiment, the second receiver 1602 receives a first information set; the first information set indicates a number of first type resources included by the first node, and a number of second type resources included by the corresponding first type resource.

[0525] As one embodiment, the second transmitter 1601 transmits a third information block; the first number depends on an indication of the third information block.

[0526] As one embodiment, the second node 1600 is a base station device.

[0527] As one embodiment, the second node 1600 is a user equipment.

[0528] As one embodiment, the second node 1600 is a TRP.

[0529] As one embodiment, the second transmitter 1601 comprises at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} in embodiment 4.

[0530] As one embodiment, the second receiver 1602 comprises at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, the memory 476} in embodiment 4.

[0531] Those skilled in the art can understand that all or part of the steps in the foregoing method can be instructed by programs to related hardware, and the programs can be stored in a computer readable storage medium, such as a read-only memory, a hard disk, an optical disk or the like. Alternatively, all or part of the steps of the foregoing embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the foregoing embodiments can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of combination of software and hardware. The user equipment, terminal and UE in the present application include but are not limited to unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebook computers, vehicle-mounted communication devices, vehicles, vehicles, RSUs, wireless sensors, network cards, Internet of Things terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base station or system device in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, small cellular base stations, home base stations, relay base stations, eNB (evolved NodeB), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, air base stations, RSUs, unmanned aerial vehicles, test equipment such as wireless communication devices that simulate part of the functions of base stations or signaling testers, and the like.

[0532] Those skilled in the art will understand that the application can be implemented by other specified forms without departing from the core or essential characteristics thereof. Therefore, the presently disclosed embodiments should in no way be considered as descriptive rather than limiting. The scope of the application is determined by the appended claims rather than the preceding description, and all modifications within the equivalent meaning and range of the claims are considered to be included therein.

Claims

1. A method in a terminal for wireless communication measurements, characterized by comprising: receiving a first information block, the first information block being configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility being equal to a first value; processing the first measurement for mobility only when the first value is not greater than a remaining number of computing capability units of the terminal; wherein the remaining number of computing capability units of the terminal is equal to a total number of computing capability units of the terminal minus a number of computing capability units already occupied; the total number of computing capability units of the terminal depends on a number of first type resources included by the terminal, and a number of second type resources included by the corresponding first type resources; any one first type resource included by the terminal includes at least one second type resource.

2. The method of claim 1, wherein, the first type resources correspond to processing units included by the terminal, the second type resources correspond to a process supported by one processing unit included by the terminal; the total number of computing capability units of the terminal corresponds to a total number of processes supported by all processing units included by the terminal.

3. The method according to claim 1 or 2, characterized in that, the first value depends on an ID of a first model, the first model being used for the first measurement for mobility, the first model being AI / ML based.

4. The method according to any one of claims 1 to 3, characterized in that comprising: receiving a second information block, the second information block being configured for a second measurement for mobility, a number of computing capability units occupied by the second measurement for mobility being equal to a second value; wherein a priority targeted by the second measurement is lower than a priority targeted by the first measurement; the first value is not greater than the remaining number of computing capability units of the terminal; the second value is not greater than the remaining number of computing capability units of the terminal; a sum of the first value and the second value is greater than the remaining number of computing capability units of the terminal; the second measurement for mobility is not processed.

5. The method according to any one of claims 1 to 4, characterized in that, the first measurement for mobility is one measurement in a first measurement set, the first measurement set including at least one of Intra-frequency measurement, Inter-frequency measurement, Inter-RAT measurement.

6. The method according to any one of claims 1 to 4, characterized in that, the first measurement for mobility is one measurement in a second measurement set, the second measurement set including at least one of cell selection, cell reselection, cell handover.

7. The method according to any one of claims 1 to 6, characterized in that, the first value is a positive integer greater than 1, the terminal determines to process the first measurement for mobility; the terminal does not assume that the computing capability units occupied by the first measurement for mobility are located in two different first type resources.

8. The method of any one of claims 1 to 7, wherein comprising: sending a first information set; wherein the first information set indicates a number of first type resources included by the terminal, and a number of second type resources included by the corresponding first type resources.

9. The method according to any one of claims 1 to 8, characterized in that comprising: receiving a second information block; wherein the first value depends on an indication of the second information block.

10. A terminal, comprising: one or more processors and a memory; The memory is coupled with the one or more processors, and is configured to store computer program codes including computer instructions, which are invoked by the one or more processors to cause the terminal to perform the method according to any one of claims 1-9.

11. A method in a base station for wireless communication measurements, characterized by Comprising: sending a first information block, the first information block being configured for a first measurement for mobility, a number of computing capability units occupied by the first measurement for mobility being equal to a first value; wherein a receiver of the first information block comprises a terminal; the terminal processes the first measurement for mobility only when the first value is not greater than a remaining number of computing capability units of the terminal; the remaining number of computing capability units of the terminal is equal to a total number of computing capability units of the terminal minus a number of computing capability units already occupied; the total number of computing capability units of the terminal depends on a number of first-type resources included by the terminal, and a number of second-type resources included by corresponding first-type resources; any one first-type resource included by the terminal comprises at least one second-type resource.

12. The method of claim 11, wherein, The first-type resources correspond to processing units included by the terminal, and the second-type resources correspond to a process supported by one processing unit included by the terminal; the total number of computing capability units of the terminal corresponds to a total number of processes supported by all processing units included by the terminal.

13. The method according to claim 11 or 12, characterized in that, The first value depends on an ID of a first model used for the first measurement for mobility, and the first model is based on AI / ML.

14. The method of any one of claims 11-13, wherein Comprising: sending a second information block, the second information block being configured for a second measurement for mobility, a number of computing capability units occupied by the second measurement for mobility being equal to a second value; wherein a priority level targeted by the second measurement is lower than a priority level targeted by the first measurement; the first value is not greater than the remaining number of computing capability units of the terminal; the second value is not greater than the remaining number of computing capability units of the terminal; a sum of the first value and the second value is greater than the remaining number of computing capability units of the terminal; and the second measurement for mobility is not processed by the terminal.

15. The method according to any one of claims 11 to 14, characterized in that, The first measurement for mobility is one measurement in a first-type measurement set, and the first-type measurement set comprises at least one of Intra-frequency measurement, Inter-frequency measurement, and Inter-RAT measurement.

16. The method of any one of claims 11-14, wherein, The first measurement for mobility is one measurement in a second-type measurement set, and the second-type measurement set comprises at least one of cell selection, cell reselection, and cell handover.

17. The method of any one of claims 11 to 16, wherein, The first value is a positive integer greater than 1, and the terminal determines to process the first measurement for mobility; and the terminal does not assume that the computing capability units occupied by the first measurement for mobility are located in two different first-type resources.

18. The method of any one of claims 11-17, wherein Comprising: receiving a first information set; The first information set indicates a number of first type resources included in the terminal and a number of second type resources included in the corresponding first type resource.

19. The method of any one of claims 11-18, wherein Comprise: sending a second information block; wherein the first value depends on an indication of the second information block. 20.A base station, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, and the one or more processors are configured to invoke the computer instructions to cause the base station to perform the method according to any one of claims 11-19.

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